Detection of Iron Nanoparticles using Nuclear Magnetic Resonance Relaxometry and Inverse Laplace Transform

Abstract

Purpose: Rapid detection of bacteria is very important in agricultural and food industries to prevent many foodborne illnesses. The objective of this study was to develop a portable nuclear magnetic resonance (NMR)-based system to detect foodborne pathogens (E. coli). This study was focused on developing a method to detect low concentrations of magnetic nanoparticles using NMR techniques. Methods: NMR relaxometry was performed to examine the NMR properties of iron nanoparticle mixtures with different concentrations by using a 1 T permanent magnet magnetic resonance imaging system. Exponential curve fitting (ECF) and inverse Laplace transform (ILT) methods were used to estimate the NMR relaxation time constants, T1 and T2, of guar gum solutions with different iron nanoparticle concentrations (0, 10 , and 10 -7 M). Results: The ECF and ILT methods did not show much difference in these values. Analysis of the NMR relaxation data showed that the ILT method is comparable to the classical ECF method and is more sensitive to the presence of iron nanoparticles. This study also showed that the spin-spin relaxation time constants acquired by a Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence are more useful for determining the concentration of iron nanoparticle solutions comparwith the spin-lattice relaxation time constants acquired by an inversion recovery pulse sequence. Conclusions: We conclude that NMR relaxometry that utilizes CPMG pulse sequence and ILT analysis is more suitable for detecting foodborne pathogens bound to magnetic nanoparticles in agricultural and food products than using inversion recovery pulse sequence and ECF analysis.